Optimization of workflow scheduling in Utility Management System with hierarchical neural network
- https://doi.org/10.2991/ijcis.2011.4.4.22How to use a DOI?
- Utility Management Systems, Hierarchical Neural Network, Grid Computing
Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.
- © 2011, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Srdjan Vukmirovic AU - Aleksandar Erdeljan AU - Imre Lendak AU - Darko Capko AU - Nemanja Nedic PY - 2011 DA - 2011/06/01 TI - Optimization of workflow scheduling in Utility Management System with hierarchical neural network JO - International Journal of Computational Intelligence Systems SP - 672 EP - 679 VL - 4 IS - 4 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2011.4.4.22 DO - https://doi.org/10.2991/ijcis.2011.4.4.22 ID - Vukmirovic2011 ER -